A Novel SMOTE-Based Classification Approach to Online Data Imbalance Problem
المؤلفون المشاركون
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-05-25
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
In many practical engineering applications, data are usually collected in online pattern.
However, if the classes of these data are severely imbalanced, the classification performance will be restricted.
In this paper, a novel classification approach is proposed to solve the online data imbalance problem by integrating a fast and efficient learning algorithm, that is, Extreme Learning Machine (ELM), and a typical sampling strategy, that is, the synthetic minority oversampling technique (SMOTE).
To reduce the severe imbalance, the granulation division for major-class samples is made according to the samples’ distribution characteristic, and the original samples are replaced by the obtained granule core to prepare a balanced sample set.
In online stage, we firstly make granulation division for minor-class and then conduct oversampling using SMOTE in the region around granule core and granule border.
Therefore, the training sample set is gradually balanced and the online ELM model is dynamically updated.
We also theoretically introduce fuzzy information entropy to prove that the proposed approach has the lower bound of model reliability after undersampling.
Numerical experiments are conducted on two different kinds of datasets, and the results demonstrate that the proposed approach outperforms some state-of-the-art methods in terms of the generalization performance and numerical stability.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Gong, Chunlin& Gu, Liangxian. 2016. A Novel SMOTE-Based Classification Approach to Online Data Imbalance Problem. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1112345
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Gong, Chunlin& Gu, Liangxian. A Novel SMOTE-Based Classification Approach to Online Data Imbalance Problem. Mathematical Problems in Engineering No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1112345
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Gong, Chunlin& Gu, Liangxian. A Novel SMOTE-Based Classification Approach to Online Data Imbalance Problem. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1112345
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1112345
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر